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Genome-Wide Linkage Scan for Physical Activity Levels in the Quebec Family Study


Medicine & Science in Sports & Exercise: August 2003 - Volume 35 - Issue 8 - pp 1355-1359
BASIC SCIENCES: Original Investigations

SIMONEN, R. L., T. RANKINEN, L. PÉRUSSE, T. RICE, D. C. RAO, Y. CHAGNON, and C. BOUCHARD. Genome-Wide Linkage Scan for Physical Activity Levels in the Quebec Family Study. Med. Sci. Sports Exerc., Vol. 35, No. 8, pp. 1355–1359, 2003.

Purpose and Methods: It is commonly recognized that there is familial aggregation for physical activity level. However, the genes and sequence variants responsible for the familial clustering have not been investigated. We performed a genome-wide linkage scan based on 432 markers typed in 767 subjects from 207 families of the Quebec Family study with the aim of identifying loci affecting physical activity levels. Four physical activity level phenotypes were used.

Results: Promising evidence of linkage (P < 0.0023) was found for physical inactivity on chromosome 2p22-p16. Suggestive linkages (0.0023<P < 0.01) were found for inactivity (7p11.2, 20q13.1), total physical activity (13q22-q31), moderate to strenuous physical activity (4q28.2, 7p11.2, 9q31.1, 13q22-q31), and time spent in physical activity (11p15 and 15q13.3).

Conclusion: This study identified several chromosomal regions harboring genes that may contribute to the propensity to be physically active or sedentary.

Numerous epidemiologic and exercise intervention studies have emphasized the importance of regular physical activity in the prevention, treatment, and rehabilitation for several common chronic diseases. Despite this evidence and physical-activity based health promotion efforts, only about one third of the population is sufficiently active to enjoy health benefits (21). This may reflect in part genetic differences in the predisposition to adopt and maintain a physically active lifestyle.

Participation in physical activity is a behavioral trait, which is mainly determined by environmental factors but which also shows a modest heritability level in a number of twin and family studies. In the Quebec Family study, the heritability estimates (consisting of genetics and shared family environmental factors) reached 25%, 16%, 19%, and 17% for the degree of inactivity, moderate to strenuous physical activity, total level of daily activity, and time spent in physical activity in the past year, respectively (34). The same trend has been found previously in the first phase of the Quebec Family study (25). Significant but low familial correlations (0.12–0.28) have been observed in the Canada Fitness Survey, which involved a total of 18,073 subjects from 1,231 families (24). However, stronger heritabilities have been found in twin studies. For example, in a Finnish intergenerational study (1), strong correlations in MZ boys (0.72) and MZ girls (0.64) were found, but exercise patterns were not strongly transmitted from one generation to the next. Other twin studies have reported heritabilities of 39% (12), 45% (17), 53% (19), and 62% (14) for a variety of physical activity phenotypes. In summary, there appears to be familial aggregation and at least a moderate genetic effect on physical activity levels.

Data on the molecular genetics of physical activity levels in humans are scarce. However, locomotor activity has been investigated in behavior genetic studies in rodents. Several mouse quantitative trait loci (QTL) for locomotor activity level have been reported in mice (5,6,9,13,18,22,26–28,35). The D2 dopamine receptor (DRD2) gene knockout mice have reduced locomotor activity level (15), and overexpression of glucose transporter 4 gene in fast-twitch skeletal muscles in mice was associated with a fourfold increase in voluntary wheel running (36). In humans, a handful of studies have reported associations between allelic variation at candidate genes and physical activity indicators in designs in which the primary phenotype was not physical activity level. For instance, in a study focusing on bone mineral density, the calcium sensing receptor (CASR) gene was associated with differences in physical activity levels among Swedish girls (20).

In this study, we present the results of the first genome-wide linkage scan to identify QTL for physical activity and inactivity phenotypes. The genome-wide scan is based on 432 markers typed in 767 subjects from 207 families of the Quebec Family study. Four physical activity level phenotypes were available for the study.

1Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA;

2Physical Activity Sciences Laboratory, Laval University, Ste-Foy, Québec, CANADA;

3Division of Biostatistics and

4Departments of Genetics and Psychiatry, Washington University, School of Medicine, St. Louis, MO; and

5Psychiatric Genetic Unit, Laval University Robert-Giffard Research Center, Québec, CANADA

Address for correspondence: Claude Bouchard, Ph.D., Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA 70808; E-mail:

Submitted for publication September 2002.

Accepted for publication January 2003.

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The subjects (N = 767) were from 207 nuclear family units of the Quebec Family study. The mean age of offspring was 30.0 (±9.3) yr and of parents 53.9 (±7.5) yr. All subjects were French-Canadians. Informed written consent was obtained from each subject, and the study protocol was approved by the Institutional Review Board of Laval University.

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Physical activity phenotypes.

Using a 3-d activity diary, which included one weekend day, subjects were instructed to record the dominant activity for each 15 min period during 24 h using a list of activities. Each period was given a score ranging from 1 to 9, with 1 corresponding to sleep and 9 to the activities characterized by the highest energy expenditure levels (3). These categorical scores were summed over each day. The test-retest reliability of the activity record among 61 subjects indicated an intraclass correlation coefficient of 0.96 over 3 d (3). Furthermore, the daily energy expenditure estimated from the diary was positively related to PWC150 and negatively correlated with fatness. Three different phenotypes were derived on the basis of this diary (34). The phenotype “inactivity” was defined by the score on resting or very light activities (categories 1–4). The phenotype of participation in “moderate to strenuous physical activities” (categories 5–9), which included light manual work as well as strenuous exercise modes or intense manual work, was also used. A phenotype labeled as “total daily activity level” was defined as the sum of all categorical values (1–9).

In addition, subjects were probed for their past year’s involvement in physical activity using a questionnaire. The questionnaire included 11 questions and several subquestions. For this study, the average number of times per week over the last year and the average duration at each session, for the physical activity most often engaged in, were extracted and used to compute the time spent on the most common physical activity (h·wk−1) during the previous year. These activities could include organized sports, walking, cycling, and any other activities with the exception of occupational activity and household chores.

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Genomic DNA was prepared from lymphoblastoid cells by the proteinase K and phenol/chloroform technique (7). A total of 432 markers were available for this genome scan. Map locations of the markers were taken from the Genetic Location Database (LDB) (http://cedar.genetics. The mean map density was 7.06 cM, being highest for chromosome 19, with an average intermarker distance of 3.72 cM, and lowest for chromosome 21, with an average intramarker distance of 11.25 cM. Polymerase chain reaction conditions and genotyping methods have been fully described elsewhere (7). LI-COR DNA sequencers (LI-COR Inc., Lincoln, NE) were used to detect the polymerase chain reaction products, and genotypes were scored automatically with the software SAGA. Genotypes were exported in a local dBASE IV database (GENEMARK) and inspected for Mendelian inheritance incompatibilities. Markers showing incompatibilities were re-genotyped completely (<10% were retyped).

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Statistical analyses.

Descriptive statistical analyses and adjustment of the phenotypes were performed using SAS (version 8.1). The phenotypes were adjusted for age, sex, and BMI in a stepwise regression procedure performed separately in six age-by-sex groups (men and women aged < 30, 30–50, and ≥ 50 yr). For estimation of regression parameters, subjects with phenotypic values ± 3 SD from the group mean were identified and temporarily set aside. However, these outliers were added back to the sample for computation of residual scores. The residuals were standardized to a mean of zero and SD of one.

Both single and multipoint linkage analyses were performed with the sib-pair linkage procedure as implemented in the SIBPAL program of S.A.G.E. 4.2 Statistical software (32). Briefly, if there is a linkage between a marker locus and a putative gene influencing a phenotype, sibs sharing a greater proportion of alleles identical-by-descent (IBD) at the marker locus also will show a greater resemblance in the phenotype. Phenotypic resemblance of the sibs, modeled as a weighted combination of squared trait difference and squared mean-corrected trait sum, is linearly regressed on the estimated proportion of alleles that the sib-pair shares IBD at each marker locus. Both single- and multi-point estimates of alleles sharing IBD were generated using the GENIBD program of S.A.G.E. Empirical P values (max. 500,000 replicates) were calculated for all markers with nominal multipoint P values of 0.01 or less. Due to the multiple statistical tests performed in a genome-wide linkage scan, the traditional alpha level of 0.05 would be too liberal. Therefore, to identify promising linkages, the alpha level P < 0.0023 in multipoint analysis was used. The alpha level of 0.0023 represents, on average, one false positive linkage for genome scan involving approximately 400 markers (see reference (31) for more details).

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The characteristics of the physical activity phenotypes in the Quebec Family study cohort are summarized in Table 1, and the most promising linkage results are shown in Table 2.

The physical inactivity phenotype showed promising linkages (multipoint P < 0.0023) with two markers (D2S2347, D2S2305) on chromosome 2p22-p16 (Fig. 1) and suggestive linkages (multipoint 0.0023<P < 0.01) on chromosomes 7p11.2 and 20q13.1 (Table 2). The time spent in physical activity phenotype was characterized by suggestive linkages with markers on chromosomes 11p15 and 15q13.3. Evidence for suggestive linkage was also found for total physical activity on chromosome 13q22-q31, and for moderate to strenuous physical activity on 4q28.2, 7p11.2, 9q31.1, and 13q22-q31 (Table 2).

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The present study is the first attempt to localize genomic regions that may harbor genes affecting human physical activity and inactivity levels. Promising evidence of linkage (P < 0.0023, detected in multipoint analysis) was found on one chromosomal region (2p22-p16). Suggestive linkages (P < 0.01) were also found with seven other chromosomal regions, among which regions 7p11.2 and 13q22 exhibited linkage with more than one physical activity phenotype. A genetic linkage is a property of a chromosomal locus, i.e., it can be used to identify chromosomal regions, which harbor genes and mutations affecting the phenotype. However, a significant linkage result provides no evidence regarding the contribution of a specific gene within the QTL to the trait variation. In the following paragraphs, we have provided a few examples of potential candidate genes located with the three QTL identified in the present study. We emphasize that these genes serve as examples only, and the chromosomal regions need to be investigated further using dense microsatellite mapping and positional cloning with single-nucleotide polymorphisms and linkage disequilibrium mapping.

The strongest evidence of linkage in the present study was observed for physical inactivity on chromosome 2p22-p16. The 20 cM region flanked by markers D2S405 and D2S2739 contains several potential candidate genes. A QTL for hereditary spastic paraplegia type 4 (SPG4) has been mapped on the same region (10,33). SPG4 is an autosomal dominant neurodegenerative disorder characterized by progressive spasticity of the lower limbs. The mean age of onset is 30 yr, but there is considerable variation in the age of onset and the severity of the symptoms. For example, Durr et al. (8) reported that 34% of the SPG4 gene carriers who were clinically affected (increased reflexes and/or extensor plantar responses) were unaware of the symptoms. SPG4 is caused by mutations in a gene encoding spastin (11), a member of the AAA family of ATPases, which is located about 1 Mb from the marker D2S2347. One could speculate that sequence variation at the SPG4 locus resulting in nonsymptomatic differences in neuromuscular controls could potentially influence the propensity to be physically inactive.

Other potential candidate genes on the region are striatin and member 1 of the solute carrier family 8 (Na/Ca exchanger 1). The striatin gene is located between markers D2S2347 and D2S2305, and it encodes an intraneuronal calmodulin-binding protein, which is expressed especially in the striatum and motoneurons (2). Inhibition of striatin by antisense oligodeoxynucleotides induced a significant decrease in nocturnal locomotor activity in rats (2). Na+/Ca2+ exchanger 1 is expressed mainly in myocardium and plays a vital role in the regulation of intracellular Ca2+ concentration in the cardiomyocytes. It removes excess calcium ions from the cell during relaxation and thereby facilitates the contraction-relaxation cycle of the myocardium. At this time, there are no data to support the contention that DNA variation in these genes could play a role on the sedentary to active continuum.

A suggestive linkage at region 13q22-q31 was found with both total daily physical activity and moderate to strenuous physical activity phenotypes. A gene encoding endothelin B receptor has been mapped on chromosome 13q22, and in rats endothelin B receptors have been shown to mediate the increase in spontaneous locomotor activity induced by treatment with a low dose of endothelin 1 (23). Moreover, the region 13q31-q34 has been previously linked with bipolar disorder, which is characterized by extreme swings in mood with occasional low activity level and lack of motivation (16).

Genome-wide linkage analysis can be used to identify chromosomal regions, which harbor genes and mutations affecting the phenotype, but a significant linkage does not indicate an association between the genetic marker and the trait of interest. Ideally, a replication of the QTL in other populations would be needed to further support the relevance of the chromosomal region for a given phenotype. Unfortunately, there are no previous linkage studies on physical activity levels in humans, and therefore the comparison of our findings with those from other populations is not possible. On the other hand, several QTL have been reported for locomotor activity in rodents (5,6,9,13,18,22,26–28,35). Using a human-mouse homology map (, we checked whether the chromosomal regions detected in the present study match with those previously reported in mice. However, no such correspondence was found. We have previously reported several QTL for endurance training-induced changes in maximal oxygen uptake (4), submaximal exercise blood pressure (29) and stroke volume (30), and body composition (7) in the HERITAGE Family study. Comparison with the results of the present study reveals that physical inactivity QTL on chromosome 2p22-p23 does not really coincide with any of the training response QTL, although linkages with stroke volume and maximal oxygen uptake changes were detected with markers about 15 cM upstream and downstream, respectively, of the inactivity QTL. These results suggest that it is unlikely that level of physical activity and responsiveness to endurance training share a common genetic background.

One should recognize that physical activity phenotypes assessed from diaries and questionnaires have clear limitations. They are commonly used in population studies where their usefulness has been repeatedly demonstrated. Indeed, physical activity level assessed by these instruments has been shown to be associated with risk of hypertension, diabetes, coronary heart disease and mortality rates. Overall, we feel confident that the QTL identified in the present genomic exploration will eventually be proven to be of significance for human variation in physical activity level. It is even possible that the evidence for genomic loci contributing to inactivity or activity phenotypes may become stronger when similar studies can be performed with more direct phenotypic measures of activity.

The genetic dissection of complex traits represents a daunting challenge. A genomic scan with a set of highly polymorphic markers is a useful strategy to identify human chromosomal regions harboring genes of interest. In the present study, the QTL for the inactivity phenotype on 2p16-p22 was supported by a level of significance sufficiently strong to warrant further exploration with additional markers, such as single nucleotide polymorphism markers. The goal would be to refine the genomic region where the QTL resides in order to undertake positional cloning of the gene(s) involved. Our results also suggest that different genomic regions are linked with different activity phenotypes. This may indicate that different domains of physical activity (e.g., inactivity vs strenuous activity) are influenced by different mechanistic pathways and therefore different genes and genomic regions are detected for these traits.

In conclusion, the results of the present genomic scan support the hypothesis that there are genes contributing to the individual differences in levels of habitual physical activity. The identification of these genes should advance our understanding of the determinants of voluntary and spontaneous activity levels. It would be desirable to replicate such studies in other populations with the goal of defining whether some of the QTL observed here are common to other ethnic groups or if they are specific to some populations or given set of environmental conditions. New insights into the mechanisms underlying a sedentary or a physically active lifestyle can be expected in the long term from these and other genetic and molecular studies.

The Quebec Family study has been supported over the years by multiple grants from the Medical Research Council of Canada (PG-11811, MT-13960, and GR-15187). This study was also supported by grants from the Academy of Finland and the Finnish Ministry of Education for R. Simonen. C. Bouchard is partially supported by the George A Bray Chair in Nutrition. The results of this paper were obtained by using the program package S.A.G.E., which is supported by a U.S. Public Health Service Resource Grant (RR03655) from the National Center for Research Resources.

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